Identification and Exploration of Immunity‐Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking, and Molecular Dynamics

作者：Pengpeng Liang, Chang Ye Yale Wang, Jiamin Liu, Hai Huang, Yue Li, Jinhua Kang, Guiyun Li, Hongyan Wu · 发表于：Immunity Inflammation and Disease · 年份：2025 · DOI：10.1002/iid3.70166 · 被引用次数：5 · 研究领域：Alzheimer's disease research and treatments、Neuroinflammation and Neurodegeneration Mechanisms、vaccines and immunoinformatics approaches

BACKGROUND: Recent research highlights the immune system's role in AD pathogenesis and promising prospects of natural compounds in treatment. This study explores immunity-related biomarkers and potential natural products using bioinformatics, machine learning, molecular docking, and kinetic simulation. METHODS: Differentially expressed genes (DEGs) in AD were analyzed using GSE5281 and GSE132903 datasets. Important AD module genes were identified using a weighted co-expression algorithm (WGCNA), and immune-related genes (IRGs) were obtained from the ImmPortPortal database. Intersecting these genes yielded important IRGs. Then, the least absolute shrinkage and selection operator (LASSO) and other methods screened common immune-related AD markers. Biological pathways were explored through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The accuracy of these markers was assessed by subject operator signature (ROC) curves and validated in the GSE122063 dataset. The datasets was then subjected to immunoinfiltration analysis. Multiple compound databases were used to analyze core Chinese medicines and components. Molecular docking and kinetic simulation verification were used for further verification. RESULTS: A total of 1360 differential genes and 5 biomarkers (PGF, GFAP, GPI, SST, NFKBIA) were identified, showing excellent diagnostic efficiency. GSEA revealed markers associated with Oxidative phosphorylation, Nicotine ad...